Local filter-based sequential and distributed fusion state estimation for nonlinear multi-sensor systems with asynchronously correlated noises

In this paper, sequential and distributed fusion state estimation algorithms based on local filter are proposed for nonlinear multi-sensor systems with asynchronously correlated noises. At first, in order to avoid the computational burden of measurement augmentation in centralized fusion filter, a sequential fusion filter is deduced by correcting measurement noises recursively. Subsequently, considering the problem that the parameters in covariance intersection (CI) fusion algorithm are difficult to estimate and the estimation accuracy of the sequential fast CI (SFCI) fusion algorithm is poor, a novel distributed fusion filter is proposed, where local filters are sent to the fusion center as the measurements and fusion predictor in the fusion center is sent to local filters by feedback. Then, to be suitable for practical applications, the numerical implementation based on third-degree spherical-radial rule is given. Finally, the superiority of the proposed algorithms is shown by using a nonlinear model.